This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.
Starting with the fundamentalsLLM architecture, tokenization, APIs, and fine-tuningthe book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevancehelping readers move confidently from basic understanding to complex applications.
Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capabilityand innovation into impact.
What you will learn:
Build intelligent chatbots and tools using open-source LLMs like GPT, LLaMA, and Mistral with guided deployment steps. Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems. Design AI agents capable of planning and executing complex workflows for automation and decision-making. Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.
Who this book is for:
Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.
Part I: Foundations of LLMs.
Chapter 1 Introduction - Large Language
Models.
Chapter 2 Inside the Transformer: Core Architectures.
Chapter 3
Fine-Tuning and Alignment.
Chapter 4 Working with LLM APIs.- Part II:
Building Intelligent Applications.
Chapter 5 Designing Your First AI
Chatbot.
Chapter 6 Retrieval-Augmented Generation (RAG).
Chapter 7 RAG
in Action: Personal Knowledge Search.
Chapter 8 Agentic AI: Beyond
Chatbots.
Chapter 9 Project: Building an Autonomous AI Agent.- Part III:
Scaling and Deploying LLM Applications.
Chapter 10 Model Serving and
Inference Optimization.
Chapter 11 Cloud, Edge, and Hybrid Deployments.-
Chapter 12 Monitoring and Observability.- Part IV: Ethics, Governance, and
the Future.
Chapter 13 Responsible AI and Safety.
Chapter 14
Governance, Compliance, and Security.
Chapter 15 The Future of LLMs and
Agentic AI.
Ajay Rawat is a Data Engineer at the Hartree Centre, STFC, UKRI, with over 20 years of experience spanning academia, industry, and professional training. His expertise includes data engineering, cloud computing, AI/LLMs, and big data technologies. A former Assistant Professor, Ajay has delivered 200+ global trainings for organizations like Google, Citibank, and Walmart, and authored research in cloud computing, fault tolerance, and AI-driven systems. He holds a Ph.D. in Computer Science and Engineering and multiple certifications across Databricks, Google Cloud, and Confluent. He is based in London, UK.
